CN105488498B - A kind of lane sideline extraction method and system based on laser point cloud - Google Patents
A kind of lane sideline extraction method and system based on laser point cloud Download PDFInfo
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- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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Abstract
A kind of lane sideline extraction method and its system based on laser point cloud of the present invention, by being carried out in high-precision electronic navigation map data element production process in the laser point cloud acquired based on traverse measurement vehicle, according to the reflected intensity of each laser point, unmanned very important lane line data element is automatically extracted with certain accuracy, production for subsequent lane grade high-precision map provides basic lane shape data, to improve the efficiency and accuracy of lane side line data element acquisition and production, it is also greatly improved the efficiency of lane grade high-precision map producing simultaneously.
Description
Technical field
The present invention relates to a kind of lane sideline extraction method and system based on laser point cloud, belongs to navigation and electronics
The crossing domain of map.
Background technique
Quick with automobile is popularized, and annual traffic accident is also more and more, and the driving safety problem of automobile becomes one
A very urgent problems, and in the active safety technologies of automobile, the prior information of high-precision space map is efficiently used,
Some potential risks are avoided in advance, are a very important active safety research and application direction.Meanwhile certainly towards the next generation
The research of dynamic driving technology is also just in expansion like a raging fire, in unmanned technology, introduces and applies high-precision map
Information carries out effective integration using the prior information of high-precision spatial map and the information of other sensors, complements each other, from
And more preferably sensing capability and path planning, guidance capability are obtained, it is a present very important research direction.
In these above-mentioned researchs, all very there is an urgent need for a kind of high-precision electronic map informations.In precision, phase
Than conditional electronic navigation map data precision generally in 1 meter to 10 meters of precision, this high-precision electronic map information exists
At least to reach decimeter grade in precision.And on model, compared to conditional electronic navigation map using road entity as abstract object, with
The data model based on correlation between description and expression road, high-precision electronic cartographic information at least will be with lane entity
For abstract object, the relationship between each data element based on lane can be described.
Therefore, it is higher not only to express precision for high-precision electronic navigation map, meanwhile, the granularity of expression is also thinner richer
Richness, so the increasing of geometric progression can be presented compared to conditional electronic navigation map for the information content of high-precision electronic navigation map expression
It is long.In this case, the difficulty of data acquisition also can be at the growth of geometric progression.Therefore, to as data elements such as lane sidelines
Element automatically extracts, with regard to becoming an extremely important problem.
Summary of the invention
In view of this, the present invention, which provides one kind, to automatically extract lane sideline, make high-precision electronic navigation ground
The higher lane sideline extraction method based on laser point cloud of the precision of figure.
A kind of lane sideline extraction method based on laser point cloud, the lane sideline based on laser point cloud are automatic
Extracting method the following steps are included:
S1, the three-dimensional laser point cloud that information of road surface is acquired by traverse measurement vehicle, and laser point cloud data is read out；
Points are less than threshold value points by S2, the set that the laser point cloud is divided into varying strength according to reflected intensity
Set is filtered, and carries out clustering to set, obtains active strength set；
S3, connectivity identification is carried out to the laser point cloud point in active strength set, finds the laser that line feature is presented
The linear connection subset of point cloud point；
S4, judge whether each linear connection subset is same line packetized elementary, and by the threadiness with same line packetized elementary
Connection subset is merged；
S5, the traverse measurement vehicle driving trace in conjunction with same position carry out lane line to fused linear connection subset
Identification.
A kind of lane sideline automatic extracting system based on laser point cloud, the lane sideline based on laser point cloud are automatic
Extraction system includes following functions module:
Point cloud data read module, the three-dimensional laser point cloud for acquiring information of road surface by traverse measurement vehicle, and to sharp
Light point cloud data is read out；
Efficient set obtains module, the set for the laser point cloud to be divided into varying strength according to reflected intensity, will
The set that points are less than threshold value points is filtered, and carries out clustering to set, obtains active strength set；
Linear subset obtains module, for carrying out connectivity identification to the laser point cloud point in active strength set, finds
The linear connection subset of the laser point cloud point of line feature is presented；
Linear subset Fusion Module, for judging whether each linear connection subset is same line packetized elementary, and will have
The linear connection subset of same line packetized elementary is merged；
Lane detection module, the traverse measurement vehicle driving trace for combining same position, even to fused threadiness
Logical subset carries out Lane detection.
Lane sideline extraction method and its system of the present invention based on laser point cloud, by being surveyed based on mobile
The laser point cloud for measuring vehicle acquisition carries out in high-precision electronic navigation map data element production process, according to the anti-of each laser point
Intensity is penetrated, unmanned very important lane line data element is automatically extracted with certain accuracy, is subsequent
The production of lane grade high-precision map provides basic lane shape data, to improve the acquisition of lane side line data element and life
The efficiency and accuracy of production, while being also greatly improved the efficiency of lane grade high-precision map producing.
Detailed description of the invention
Fig. 1 is the flow diagram of the lane sideline extraction method of the present invention based on laser point cloud；
Fig. 2 is the flow diagram of step S2 in Fig. 1；
Fig. 3 is the flow diagram of step S23 in Fig. 2；
Fig. 4 is the flow diagram of step S3 in Fig. 1；
Fig. 5 is the flow diagram of step S31 in Fig. 4；
Fig. 6 is the flow diagram of step S32 in Fig. 4；
Fig. 7 is the flow diagram of step S4 in Fig. 1；
Fig. 8 is the flow diagram of step S5 in Fig. 1；
Fig. 9 is the module frame chart of the lane sideline automatic extracting system of the present invention based on laser point cloud.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated, it should be understood that and the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
As shown in Figure 1, the embodiment of the present invention provides a kind of lane sideline extraction method based on laser point cloud, it is described
Lane sideline extraction method based on laser point cloud the following steps are included:
S1, the three-dimensional laser point cloud that information of road surface is acquired by traverse measurement vehicle, and laser point cloud data is read out；
Points are less than threshold value points by S2, the set that the laser point cloud is divided into varying strength according to reflected intensity
Set is filtered, and carries out clustering to set, obtains active strength set；
S3, connectivity identification is carried out to the laser point cloud point in active strength set, finds the laser that line feature is presented
The linear connection subset of point cloud point；
S4, judge whether each linear connection subset is same line packetized elementary, and by the threadiness with same line packetized elementary
Connection subset is merged；
S5, the traverse measurement vehicle driving trace in conjunction with same position carry out lane line to fused linear connection subset
Identification.
The clustering is mainly clustered and is filtered to the point in cloud according to the reflected intensity of laser point, thus
It would be impossible to provide strength type to be selected as noise remove, and for the identification of subsequent lane line there are the point cloud of linear element,
Specific steps are as shown in Figure 2:
The step S2 include it is following step by step；
S21, the laser point cloud is divided by multiple and different intensity set according to reflected intensity；
S22, setting threshold value are counted, and the points of each intensity set and threshold value points are compared, if intensity set
Points less than threshold value count, then delete the intensity set, remaining strength set is combined into active strength set；
All active strength set are carried out clustering according to cluster intensity threshold by S23, setting cluster intensity threshold,
Until the reflected intensity of laser point cloud is respectively less than cluster intensity threshold in each active strength set；
S24, setting cluster threshold value points, by the active strength set after progress clustering, points are less than cluster threshold
The active strength set of value points is deleted.
Wherein, as shown in figure 3, the step S2 3 include it is following step by step；
S231, setting cluster intensity threshold calculate to be discriminated using all active strength set as a subclass to be discriminated
The average reflection intensity and reflected intensity mean square deviation of subclass.
If the reflected intensity mean square deviation of S232, subclass to be discriminated is greater than cluster intensity threshold, will be in subclass to be discriminated
Point according to reflected intensity distance center, and with the left and right one reflected intensity mean square deviation in interval of reflected intensity distance center
Subclass to be identified is divided into three subclasses by the rule of three point distances；
If the mean square deviation of the reflected intensity of S233, subclass to be discriminated is less than cluster intensity threshold, the subclass to be discriminated
Complete cluster；
S234, intensity threshold is clustered up to the reflected intensity of all subclasses to be discriminated is respectively less than.
Specifically, successively traversing all the points, and strong according to the reflection of point in the laser point cloud data obtained according to step S1
Angle value classifies to the point in cloud, is denoted as PCi, i=1,2 ..., n, wherein reflected intensity class PCiThe reflection of middle all the points
Intensity is identical.Remember reflected intensity class PCiIn points beAll points are Num in laser point cloud0。
Traverse reflected intensity class PCi, i=1,2 ..., n, if PCiIn pointsLess than threshold value points MIN_PT_
(MIN_PT_NUM is used to describe to constitute the noise reflection point number of road element expression to NUM, is a lesser numerical value threshold
Value, such as, but not limited to 100), then deletes the PCiIn all the points, and adjust point Yun to be processed and always count
Setting cluster intensity threshold CLUSTER__INV, using all active strength set as a subclass to be discriminated, meter
Calculate the average reflection intensity AVG_INV and reflected intensity mean square deviation STDEV_INV of subclass to be discriminated.
If the mean square deviation of the reflected intensity of subclass to be discriminated, which is greater than, clusters intensity threshold, i.e. STDEV_INV >
CLUSTER__INV.Then by the point in subclass to be discriminated according to reflected intensity distance AVG_INV-STDEV_INV, AVG_INV,
AVG_INV+STDEV_INV nearest principle, by subclass to be identified be divided into left (AVG_INV-STDEV_INV), in (AVG_
INV), right (AVG_INV+STDEV_INV) three subclasses.
Otherwise, if the mean square deviation of the reflected intensity of subclass to be discriminated, which is not more than, clusters intensity threshold, i.e. STDEV_INV≤
CLUSTER__INV, then the subclass to be discriminated completes cluster,
Wherein CLUSTER__INV is the threshold value of end of clustering, indicates that the reflected intensity of all the points in a subclass all compares
It is close, generally desired value should be selected according to the range of laser reflection intensity, so that whole clusters number is at more than ten or so, example
Such as, but not limited to, the point cloud for reflected intensity 60,000 or so, CLUSTER__INV=1000.
According to the method described above, until the reflected intensity of all subclasses to be discriminated, which is respectively less than, clusters intensity threshold, obtained institute
Having subclass is CCi, i=1,2 ..., m, subclasses C CiThe sum at midpoint is NumCi, total points of all subclasses are Num0。
Preliminary screening is carried out to each subclass that cluster obtains, on the contrary it will not be possible to be the subclass removal of Road, i.e., will own
Meet NumCi> α * Num0Subclass removal, wherein α be the coefficient for differentiating the expression of non-linear shape element, and value range is 0 to 1
Real number, but it is general should not be too small, such as, but not limited to α=0.33.
Wherein, as shown in figure 4, the step S3 include it is following step by step；
S31, connectivity identification is carried out to the point in active strength set, establishes the connection subset of each point.
S32, it carries out curve fitting to the connection subset of each laser point cloud point, identifies whether to be connected to subset for threadiness.
As shown in figure 5, the step S31 include it is following step by step；
S311, a point in active strength set is randomly selected, a connection subset is established based on the point；
S312, setting connection threshold value calculate in the active strength set all distances with the point less than described and are connected to threshold value
Point, and be added into the connection subset；
S313, pass sequentially through connection threshold value establish multiple connection subsets, until the active strength set in all the points belong to
In one of connection subset.
As shown in fig. 6, the step S32 include it is following step by step；
S321, it carries out curve fitting to the point for belonging to a connection subset；
S322, average distance of all the points in the connection subset with respect to matched curve is calculated；
If S323, average distance are less than connection threshold value, which is that a linear connection subset otherwise should
Connection subset is not linear connection subset.
Specifically, randomly selecting a untreated point in active strength set, it is denoted as P0, and be P0Establish new connection
Subset CON_0.
Setting connection threshold value, calculates all and P in the active strength set0Point distance is less than connection threshold value CONN_
Point { the P of THRESHOLDjJ=1,2 ..., k }, then by { PjJ=1,2 ..., k in it is all not connection subset CON_0 in points
Connection subset CON_0 is added.
It carries out curve fitting, and is calculated in the connection subset to the point in each connection subset, belonging to a connection subset
All the points with respect to the average distance of matched curve, be denoted as AVG_ERROR, if AVG_ERROR < CONN_THRESHOLD,
The connection subset is a linear connection subset, and otherwise, which is not linear connection subset.
Wherein, as shown in fig. 7, the step S4 include it is following step by step；
S41, a benchmark threadiness connection subset in active strength set is randomly selected, finds and matches in other connection subsets
Subset is connected to threadiness；
Point in S42, the linear connection subset of the pairing is connected to the average departure of the analytical expression of subset to benchmark threadiness
The threadiness of the same shape is expressed even from linear one for being connected to subset on the basis of less than threshold value is connected to, then matching threadiness connection subset
Logical subset.
S43, it all pairing threadiness connection subsets be connected to subset with benchmark threadiness merges, until all threadiness companies
Logical subset all complete by processing.
Specifically, randomly selecting a untreated threadiness in active strength set is connected to subset CON_I as reference line
Shape is connected to subset, is found in the linear connection subset CON_J:CON_J of all pairings for meeting following condition in other subclasses
The average distance that point arrives the analytical expression of CON_I is less than CONN_THRESHOLD, and CON_J is that an expression of CON_I is same
All threadiness for expressing the same shape with CON_I are connected to subset and merged by the linear connection subset of shape.
Wherein, as shown in figure 8, the step S5 include it is following step by step；
S51, a fused linear connection subset is chosen, loads the traverse measurement vehicle driving trace of same position；
S52, projection of the threadiness connection subset in traveling trajectory line is calculated, and calculates the length of view field；
S53, setting Lane detection threshold value, if the length of view field is connected to subset matched curve itself with the threadiness
The ratio of length is greater than Lane detection threshold value, then judges the linear connection subset for lane line；
S54, it carries out curve fitting again to the linear connection subset for being identified as lane line, obtains the shape line of the lane line
And width.
Specifically, linear connection subset to be identified after choosing a fusion, loads the traverse measurement garage of same position
Sail track.Then projection of the threadiness connection subset in traveling trajectory line is calculated, and calculates the length of view field, is denoted as
PRJ_LEN.The length of threadiness connection subset matched curve itself is denoted as FIT_LEN.If PRJ_LEN/FIT_LEN >
REC_THRESHOD is then identified as lane line.
Wherein, the REC_THRESHOD is Lane detection threshold value, and expression is the one of matched curve and driving trace
Cause property, generally close to 1 real number, such as, but not limited to REC_THRESHOD=0.85.
It carries out curve fitting again to the linear connection subset for being identified as lane line, the shape line of the lane line can be obtained
And width.
The lane sideline automatic extracting system based on laser point cloud that the present invention also provides a kind of, as shown in figure 9, the base
In the lane sideline automatic extracting system of laser point cloud include following functions module:
Point cloud data read module 10, the three-dimensional laser point cloud for acquiring information of road surface by traverse measurement vehicle, and it is right
Laser point cloud data is read out；
Efficient set obtains module 20, the set for the laser point cloud to be divided into varying strength according to reflected intensity,
The set that points are less than threshold value points is filtered, and clustering is carried out to set, obtains active strength set；
Linear subset obtains module 30, for carrying out connectivity identification to the laser point cloud point in active strength set, looks for
To the linear connection subset for the laser point cloud point that line feature is presented；
Linear subset Fusion Module 40, for judging whether each linear connection subset is same line packetized elementary, and will tool
There is the linear connection subset of same line packetized elementary to be merged；
Lane detection module 50, the traverse measurement vehicle driving trace for combining same position, to fused threadiness
It is connected to subset and carries out Lane detection.
Lane sideline extraction method and its system of the present invention based on laser point cloud, by being surveyed based on mobile
The laser point cloud for measuring vehicle acquisition carries out in high-precision electronic navigation map data element production process, according to the anti-of each laser point
Intensity is penetrated, unmanned very important lane line data element is automatically extracted with certain accuracy, is subsequent
The production of lane grade high-precision map provides basic lane shape data, to improve the acquisition of lane side line data element and life
The efficiency and accuracy of production, while being also greatly improved the efficiency of lane grade high-precision map producing.
Related terms of the present invention are explained:
1. high-precision electronic navigation map
Relatively traditional using road as the precision of basic element is the navigation map of meter level, the precision provided be decimetre even
Centimeter Level, using lane as basic element, towards unmanned and active safety application function next-generation digital navigation map.
2. traverse measurement vehicle
Laser scanner, panorama camera, high accuracy positioning equipment and High Accuracy Inertial equipment are installed, are capable of providing high-precision
Spend the measurement vehicle of location information.
3. laser point cloud
The laser point cloud that the laser scanner scans with location information and reflected intensity of traverse measurement vehicle acquisition obtain.
Point cloud is referred to as in text.As shown in Figure 2.
4. laser point cloud point
Each point in laser point cloud, referred to herein as laser point cloud point, also referred to as point.
5. lane sideline
Lane both sides printing sideline, text in also referred to as lane line, as shown in Figure 3.
6. reflected intensity set
The set of all the points with identical reflected intensity, referred to as a reflected intensity set in point cloud.
7. being connected to subset
Only in a specific subset (such as reflected intensity set) for cloud or point cloud, interconnected all the points are constituted
A subset.
8. threadiness connection subset
If the global shape of a connection subset shows line feature, it can be seen as the expression of a curve, then
Referred to as one linear connection subset.
Apparatus above embodiment and embodiment of the method are one-to-one, the simple places of Installation practice, referring to method reality
Apply example.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.
Professional further appreciates that, unit described in conjunction with the examples disclosed in the embodiments of the present disclosure
And algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware and
The interchangeability of software generally describes each exemplary composition and step according to functionality in the above description.This
A little functions are implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Specially
Industry technical staff can use different methods to achieve the described function each specific application, but this realization is not
It should be more than the scope of the present invention.
The step of method described in conjunction with the examples disclosed in this document or algorithm, can directly be held with hardware, processor
The combination of capable software module or the two is implemented.Software module can be placed in random access memory, memory, read-only memory,
Electrically programmable ROM, electricity can sassafras except in programming ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field institute it is public
In the storage medium for any other forms known.
The embodiment of the present invention is described with above attached drawing, but the invention is not limited to above-mentioned specific
Embodiment, the above mentioned embodiment is only schematical, rather than restrictive, those skilled in the art
Under the inspiration of the present invention, without breaking away from the scope protected by the purposes and claims of the present invention, it can also make very much
Form, all of these belong to the protection of the present invention.
Claims (7)
1. a kind of lane sideline extraction method based on laser point cloud, which is characterized in that the vehicle based on laser point cloud
Road sideline extraction method the following steps are included:
S1, the three-dimensional laser point cloud that information of road surface is acquired by traverse measurement vehicle, and laser point cloud data is read out；
Points are less than the set of threshold value points by S2, the set that the laser point cloud is divided into varying strength according to reflected intensity
It is filtered, and clustering is carried out to set, obtain active strength set；
S3, connectivity identification is carried out to the laser point cloud point in active strength set, finds the laser point cloud that line feature is presented
The linear connection subset of point；
S4, judge whether each linear connection subset is same line packetized elementary, and the threadiness with same line packetized elementary is connected to
Subset is merged；
S5, the traverse measurement vehicle driving trace in conjunction with same position carry out Lane detection to fused linear connection subset；
Wherein, the step S2 include it is following step by step；
S21, the laser point cloud is divided by multiple and different intensity set according to reflected intensity；
S22, setting threshold value are counted, and the points of each intensity set and threshold value points are compared, if the point of intensity set
Number is less than threshold value and counts, then deletes the intensity set, remaining strength set is combined into active strength set；
All active strength set are carried out clustering according to cluster intensity threshold by S23, setting cluster intensity threshold, until
The reflected intensity of laser point cloud, which is respectively less than, in each active strength set clusters intensity threshold；
S24, setting cluster threshold value points, by the active strength set after progress clustering, points are less than cluster threshold point
Several active strength set is deleted；
The step S5 include it is following step by step；
S51, a fused linear connection subset is chosen, loads the traverse measurement vehicle driving trace of same position；
S52, projection of the threadiness connection subset in traveling trajectory line is calculated, and calculates the length of view field；
S53, setting Lane detection threshold value, if the length of view field is connected to subset matched curve length itself with the threadiness
Ratio be greater than Lane detection threshold value, then judge the linear connection subset for lane line；
S54, it carries out curve fitting again to the linear connection subset for being identified as lane line, obtains the shape line and width of the lane line
Degree.
2. the lane sideline extraction method based on laser point cloud according to claim 1, which is characterized in that the step
S23 include it is following step by step；
S231, setting cluster intensity threshold calculate subclass to be discriminated using all active strength set as a subclass to be discriminated
Average reflection intensity and reflected intensity mean square deviation；
If the reflected intensity mean square deviation of S232, subclass to be discriminated is greater than cluster intensity threshold, by the point in subclass to be discriminated
According to three with reflected intensity distance center, and with the one reflected intensity mean square deviation in the left and right interval of reflected intensity distance center
The far and near rule of point, is divided into three subclasses for subclass to be identified；
If the mean square deviation of the reflected intensity of S233, subclass to be discriminated is less than cluster intensity threshold, which is completed
Cluster；
S234, intensity threshold is clustered up to the reflected intensity of all subclasses to be discriminated is respectively less than.
3. the lane sideline extraction method based on laser point cloud according to claim 1, which is characterized in that the step
S3 include it is following step by step；
S31, connectivity identification is carried out to the point in active strength set, establishes the connection subset of each point；
S32, it carries out curve fitting to the connection subset of each laser point cloud point, identifies whether to be connected to subset for threadiness.
4. the lane sideline extraction method based on laser point cloud according to claim 3, which is characterized in that the step
S31 include it is following step by step；
S311, a point in active strength set is randomly selected, a connection subset is established based on the point；
S312, setting connection threshold value, calculate in the active strength set all distances with the point less than the point for being connected to threshold value,
And it is added into the connection subset；
S313, pass sequentially through connection threshold value establish multiple connection subsets, until the active strength set in all the points belong to it
In in a connection subset.
5. the lane sideline extraction method based on laser point cloud according to claim 3, which is characterized in that the step
S32 include it is following step by step；
S321, it carries out curve fitting to the point for belonging to a connection subset；
S322, average distance of all the points in the connection subset with respect to matched curve is calculated；
If S323, average distance are less than connection threshold value, which is a linear connection subset, otherwise, the connection
Subset is not linear connection subset.
6. the lane sideline extraction method based on laser point cloud according to claim 1, which is characterized in that the step
S4 include it is following step by step；
S41, a benchmark threadiness connection subset in active strength set is randomly selected, finds pairing line in other connection subsets
Shape is connected to subset；
The average distance for the analytical expression that S42, the linear point being connected in subset of the pairing to benchmark threadiness are connected to subset is small
In connection threshold value, then linear connection of linear expression the same shape for being connected to subset on the basis of linear connection subset is matched
Collection；
S43, all pairing threadiness connection subsets be connected to subset with benchmark threadiness merge, until it is all it is linear be connected to it is sub
Collection all complete by processing.
7. a kind of lane sideline automatic extracting system based on laser point cloud, which is characterized in that the vehicle based on laser point cloud
Road sideline automatic extracting system includes following functions module:
Point cloud data read module, the three-dimensional laser point cloud for acquiring information of road surface by traverse measurement vehicle, and to laser point
Cloud data are read out；
Efficient set obtains module, the set for the laser point cloud to be divided into varying strength according to reflected intensity, will count
Set less than threshold value points is filtered, and carries out clustering to set, obtains active strength set；
Linear subset obtains module, for carrying out connectivity identification to the laser point cloud point in active strength set, finds presentation
The linear connection subset of the laser point cloud point of line feature；
Linear subset Fusion Module, for judging whether each linear connection subset is same line packetized elementary, and will have identical
The linear connection subset of linear element is merged；
Lane detection module, the traverse measurement vehicle driving trace for combining same position, to fused linear connection
Collection carries out Lane detection；
Wherein, the efficient set obtains module and is specifically used for:
The laser point cloud is divided into multiple and different intensity set according to reflected intensity；
Threshold value is arranged to count, the points of each intensity set and threshold value points are compared, if the points of intensity set are small
It counts in threshold value, then deletes the intensity set, remaining strength set is combined into active strength set；
Setting cluster intensity threshold carries out clustering to all active strength set according to cluster intensity threshold, until each
The reflected intensity of laser point cloud, which is respectively less than, in a active strength set clusters intensity threshold；
Setting cluster threshold value points, by the active strength set after progress clustering, points are less than cluster threshold value points
Active strength set is deleted；
The Lane detection module is specifically used for:
A fused linear connection subset is chosen, the traverse measurement vehicle driving trace of same position is loaded；
Projection of the threadiness connection subset in traveling trajectory line is calculated, and calculates the length of view field；
Lane detection threshold value is set, if the ratio of the length of view field and threadiness connection subset matched curve length itself
Value is greater than Lane detection threshold value, then judges the linear connection subset for lane line；
It carries out curve fitting again to the linear connection subset for being identified as lane line, obtains the shape line and width of the lane line.
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| CN114662600B (en) * | 2022-03-25 | 2023-11-07 | 南京慧尔视软件科技有限公司 | Lane line detection method, device and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101608924A (en) * | 2009-05-20 | 2009-12-23 | 电子科技大学 | A kind of method for detecting lane lines based on gray scale estimation and cascade Hough transform |
| CN104197897A (en) * | 2014-04-25 | 2014-12-10 | 厦门大学 | Urban road marker automatic sorting method based on vehicle-mounted laser scanning point cloud |
| WO2015043510A1 (en) * | 2013-09-27 | 2015-04-02 | 比亚迪股份有限公司 | Lane line detection method and system, and method and system for lane deviation prewarning |
| CN104766058A (en) * | 2015-03-31 | 2015-07-08 | 百度在线网络技术（北京）有限公司 | Method and device for obtaining lane line |
-
2016
- 2016-01-15 CN CN201610027682.4A patent/CN105488498B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101608924A (en) * | 2009-05-20 | 2009-12-23 | 电子科技大学 | A kind of method for detecting lane lines based on gray scale estimation and cascade Hough transform |
| WO2015043510A1 (en) * | 2013-09-27 | 2015-04-02 | 比亚迪股份有限公司 | Lane line detection method and system, and method and system for lane deviation prewarning |
| CN104197897A (en) * | 2014-04-25 | 2014-12-10 | 厦门大学 | Urban road marker automatic sorting method based on vehicle-mounted laser scanning point cloud |
| CN104766058A (en) * | 2015-03-31 | 2015-07-08 | 百度在线网络技术（北京）有限公司 | Method and device for obtaining lane line |
Non-Patent Citations (2)
| Title |
|---|
| 基于Beamlet和K-means聚类的车道线识别;肖进胜等;《四川大学学报(工程科学版)》;20150720;第47卷(第4期);第98-103页 |
| 基于扩展卡尔曼滤波器的车道线检测算法;彭红等;《光电子·激光》;20150315;第26卷(第3期);第567-574页 |
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